The performance of deep-learning-based equipment fault detection systems relies heavily on the size and class diversity of the sample set. Because it is difficult to collect all types of fault sample comprehensively in industrial production， there is a demand for sample set augmentation. A fault dataset amplification method combining 3D modeling with an improved cycle generative adversarial network （CycleGAN） is proposed. First， various equipment malfunction images generated by 3D modeling software are applied to the CycleGAN network training to guide it in generating pseudo-real images to address the problem of insufficient samples and an uneven distribution. Second， a U-ResNet generator is used in the CycleGAN network to solve the problem of edge blurring and gradient vanishing during network training. The method was applied to the task of belt conveyor deviation detection. The experimental results show that the contour structure of the method converges quickly in the training process and has good timeliness in comparison with other amplification methods. The accuracy rate of the method is 98.1% when applying to the target detection network， which is 4.5% higher than that of the original real dataset. It meets the basic requirements of a balanced distribution of amplified datasets and high image quality.